**4. Black box models and AI in medicine**

Investigating unmeasured risk factors can improve the modelling of disease progression and thus enable clinicians to focus on early diagnosis and treatment of unexpected conditions. However, the overuse of hidden variables and lack of explainability can lead to complex models, which are not well understood (being black box in nature). Models need to be understood by clinicians to facilitate transparency and trust.

#### **4.1 Explainability in deep learning**

This stage outlines and discusses the limitations of Deep Learning approaches that have been proposed, so far, to gain deeper insights into the understanding of black box AI models. AI medical machine such as Deep Learning has become ubiquitous to provide a high-performance prediction. Nevertheless, understanding their mechanisms has become a significant concern worldwide whereby the goal is to gain clinicians and patients trust. The reason behind this is due to several

obstacles that arise to interpret the findings, such as the scale of big data, complex interactions, and high-dimensional internal state.
